Unité de Bioinformatique génomique et structurale


[ Protein dataset, List of Entries, Performances of PRELUDE&Fugue, Performances of Prelude&FUGUE ]

PROTEIN DATASET

The protein dataset used to derive the potentials is described in [1]. It is derived from an intial set of 1522 high-resolution X-Ray structures of protein chains with less than 20% pairwise sequence identity, extracted from the PISCES website [2] (http:/dunbrack.fccc.edu/Guoli/pisces_download.php). From these, all structures containing more than 5% heteroatoms or non-natural residues were excluded. The chains with missing residues were divided into continuous fragments, and only the fragments of 50 residues at least were kept. This led to a dataset of 1537 fragments from 1403 protein chains.

    [1] Development of novel statistical potentials describing cation-pi interactions in proteins and comparison with semiempirical and quantum chemistry approaches
    D. Gilis, C. Biot, E. Buisine, Y. Dehouck and M. Rooman
    J. Chem. Inf. Model. (in press)

    [2] PISCES: recent improvements to a PDB sequence culling server.
    Wang G, Dunbrack RL Jr.
    Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W94-8.
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PERFORMANCES OF PRELUDE&Fugue

  • Position score

  • Prelude&Fugue was applied to predict the structure of each protein of the dataset, using the jack knife crossvalidation procedure where the target protein is removed from the dataset when deriving the energy functions. The percentage of correctly predicted positions for each of the seven structural states A, C, B, P, G, E, and O, as well as the average score, are given in the table below. The specificities (sometimes called positive predictive values) are defined as the ratio of the number of correctly predicted states over the number of predicted states, whereas the sensitivities correspond to the ratio of the number of correctly predicted states over the number of observed states.

        
        

    7 states | Specificity |Sensitivity ||   3 states |Specificity|Sensitivity

    A |65%|57%||  A or C |68% | 69%
    C |31%|40%|| 

    B |53%|50%||  B or P |66% | 59%
    P |39%|32%|| 

    G |35%|44%||  G or E or O |38% | 71%
    E |46%|64%|| 
    O |7%|13%|| 

    Average |47%|48%|| Average |64%|65% 

    The scores without distinction between the helical conformations A and C, between the extended states B and P, and between the other conformations G, E and O, are also given. These scores must be compared to the corresponding random scores, which, in the case of the specificities, are equal to 47% for the 7 states, and 41% for the 3 states.

  • Segment score

  • The predicted conformations can be divided into segments corresponding to successions of identical structural states. The segment score is defined as the fraction of correctly predicted positions in the segment. The green curve in the Figure below corresponds to the average segment score as a function of the segment length. As alpha-helices correspond to successions of A and C states, and beta-strands to stretches of B and P states, we also define the segment score for 3 states (A or C), (B or P), or (G or E or O). The red curve represents the 3-state segment score as a function of the segment length.



    In the next two Figures, the segment scores are given as a function of the fraction of segments of fixed length that have at least that score, considering either 3 or 7 structural states. We see, for example, that 60% of the segments of length 12 have at least 60% correctly 7-state predicted positions :




    Similarly, 63% of the segments of length 7 have at least 60% correctly 3-state predicted positions :

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    PERFORMANCES OF Prelude&FUGUE

  • Position score

  • Prelude&Fugue was applied to predict the structure of each protein of the dataset, using the jack knife crossvalidation procedure. This program leads to a prediction for about 30% of the positions. Among these, the percentage of correctly predicted positions for each of the seven structural states, as well as the average score, are given in the table below. The specificities (sometimes called positive predictive values) are defined as the ratio of the number of correctly predicted states over the number of predicted states, whereas the sensitivities correspond to the ratio of the number of correctly predicted states over the number of observed states.

        
        

    7 states | Specificity |Sensitivity ||   3 states |Specificity|Sensitivity

    A |77%|85%||  A or C |83% | 85%
    C |36%|26%|| 

    B |66%|67%||  B or P |79% | 69%
    P |50%|29%|| 

    G |36%|52%||  G or E or O |30% | 54%
    E |24%|65%|| 
    O |9%|13%|| 

    Average |66%|65%|| Average |77%|78% 

    The scores without distinction between the helical conformations A and C, between the extended states B and P, and between the other conformations G, E and O, are also given.

  • Segment score

  • The predicted conformations can be divided into segments corresponding to successions of identical structural states. The segment score is defined as the fraction of correctly predicted positions in the segment. The green curve in the Figure below corresponds to the average segment score as a function of the segment length. As alpha-helices correspond to successions of A and C states, and beta-strands to stretches of B and P states, we also define the segment score for 3 states (A or C), (B or P), or (G or E or O). The red curve represents the 3-state segment score as a function of the segment length.




    In the next two Figures, the segment scores are given as a function of the fraction of segments of fixed length that have at least that score, considering either 3 or 7 structural states. We see, for example, that 60% of the segments of length 10 have at least 60% correctly 7-state predicted positions :


    Similarly, 70% of the segments of length 8 have at least 60% correctly 3-state predicted positions :

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